Over-adjustment or miscomprehension? A re-examination of the jumping to conclusions bias
Bibliographic record
Abstract
OBJECTIVE: Previous research has consistently shown that individuals with delusions typically exhibit a jumping to conclusions (JTC) bias when administered the probabilistic reasoning 'beads task' (i.e. decisions made with limited evidence or 'premature decisions' and decisions over-adjusted in light of disconfirming evidence or 'over-adjustment'). More recent work, however, also suggests that these effects may also be influenced by miscomprehension of the task. The current paper is an investigation into the contributing effects of miscomprehension on the JTC bias. METHOD: A total of 75 participants (25 diagnosed with schizophrenia with a history of delusions; 25 non-clinical delusion-prone; 25 non-delusion-prone controls) completed two identical versions of the beads task, distinct only by the inclusion of an extra instructional set designed to increase comprehension. RESULTS: Qualitative data confirmed that miscomprehension is a valid construct, and the results showed that the addition of an instructional set to the second version of the task led to greater comprehension and a statistically significant drop in 'over-adjustment'. Nevertheless, both tasks showed that 'premature decisions' were significantly more prevalent in the schizophrenia group and were unaffected by the intervention. CONCLUSIONS: It was concluded that the 'premature decisions' component of the JTC bias remains a feature of decision-making in schizophrenia, but that previously reported 'over-adjustment' effects are likely to be influenced by miscomprehension of the beads task instructional set. These findings are discussed in light of the recently proposed 'hypersalience of evidence-hypothesis matches' account of the JTC bias.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".